Temporal Knowledge Graph Hyperedge Forecasting: Exploring Entity-to-Category Link Prediction

TL;DR

This paper introduces an explainable rule-based approach for predicting future components in temporal knowledge graphs, incorporating entity categories and leveraging LLMs for unknown category generation, enhancing transparency over traditional embedding methods.

Contribution

It extends the TLogic framework by integrating entity categories and proposing an LLM-based method for unknown category generation, improving explainability and relevance in predictions.

Findings

High accuracy with explainable rules

Effective LLM-based category generation

Improved relevance in entity prediction

Abstract

Temporal Knowledge Graphs have emerged as a powerful way of not only modeling static relationships between entities but also the dynamics of how relations evolve over time. As these informational structures can be used to store information from a real-world setting, such as a news flow, predicting future graph components to a certain extent equates predicting real-world events. Most of the research in this field focuses on embedding-based methods, often leveraging convolutional neural net architectures. These solutions act as black boxes, limiting insight. In this paper, we explore an extension to an established rule-based framework, TLogic, that yields a high accuracy in combination with explainable predictions. This offers transparency and allows the end-user to critically evaluate the rules applied at the end of the prediction stage. The new rule format incorporates entity category as a key component with the purpose of limiting rule application only to relevant entities. When categories are unknown for building the graph, we propose a data-driven method to generate them with an LLM-based approach. Additionally, we investigate the choice of aggregation method for scores of retrieved entities when performing category prediction.